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We investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Question Generation (DQG).
Answering and questioning for machine reading
Lucy Vanderwende · 2007
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Good question! statistical ranking for question generation
Michael Heilman and Noah A. Smith · 2010
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Generating natural language questions to support learning on-line
David Lindberg, Fred Popowich, John C. Nesbit, and Philip H. Winne · 2013
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Linguistic considerations in automatic question generation
Karen Mazidi and Rodney D. Nielsen · 2014
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Deep questions without deep understanding
Igor Labutov, Sumit Basu, and Lucy Vanderwende · 2015
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Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning · 2015
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Generating natural questions about an image
Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin, Margaret Mitchell, Xiaodong He, and Lucy Vanderwende · 2016
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Squad: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy S. Liang · 2016
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Generating factoid questions with recurrent neural networks: The 30m factoid question-answer corpus
Iulian Serban, Alberto García-Durán, Çaglar Gülçehre, Sungjin Ahn, A. P. Sarath Chandar, Aaron C. Courville, and Yoshua Bengio · 2016
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On generating characteristic-rich question sets for qa evaluation
Yu Su, Huan Sun, Brian Sadler, Mudhakar Srivatsa, Izzeddin Gur, Zenghui Yan, and Xifeng Yan · 2016
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A syntactic approach to domain-specific automatic question generation
Guy Danon and Mark Last · 2017
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Learning to ask: Neural question generation for reading comprehension
Xinya Du, Junru Shao, and Claire Cardie · 2017
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Race: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning · 2017
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Bidirectional attention flow for machine comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi · 2017
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Harvesting paragraph-level question-answer pairs from wikipedia
Xinya Du and Claire Cardie · 2018
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Automating reading comprehension by generating question and answer pairs
Vishwajeet Kumar, Kireeti Boorla, Yogesh Meena, Ganesh Ramakrishnan, and Yuan-Fang Li · 2018
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Quase: Sequence editing under quantifiable guidance
Yi Liao, Lidong Bing, Piji Li, Shuming Shi, Wai Lam, and Tong Zhang · 2018
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Leveraging context information for natural question generation
Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, and Daniel Gildea · 2018
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Answer-focused and position-aware neural question generation
Xingwu Sun, Jing Liu, Yajuan Lyu, Wei He, Yanjun Ma, and Shi Wang · 2018
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Gaan: Gated attention networks for learning on large and spatiotemporal graphs
Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung · 2018
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Style transfer from non-parallel text by cross-alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi S. Jaakkola · 2017
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Evaluation metrics for machine reading comprehension: Prerequisite skills and readability
Saku Sugawara, Yusuke Kido, Hikaru Yokono, and Akiko Aizawa · 2017
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Gated self-matching networks for reading comprehension and question answering
Wenhui Wang, Nan Yang, Furu Wei, Baobao Chang, and Ming Zhou · 2017
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Machine comprehension by text-to-text neural question generation
Xingdi Yuan, Tong Wang, Çaglar Gülçehre, Alessandro Sordoni, Philip Bachman, Sandeep Subramanian, Saizheng Zhang, and Adam Trischler · 2017
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Neural question generation from text: A preliminary study
Qingyu Zhou, Nan Yang, Furu Wei, Chuanqi Tan, Hangbo Bao, and Ming Zhou · 2017
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Paragraph-level neural question generation with maxout pointer and gated self-attention networks
Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, and Qifa Ke · 2018
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Title-guided encoding for keyphrase generation
Wang Chen, Yifan Gao, Jiani Zhang, Irwin King, and Michael R. Lyu · 2019
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Generating distractors for reading comprehension questions from real examinations
Yifan Gao, Lidong Bing, Piji Li, Irwin King, and Michael R. Lyu · 2019
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